Review of Different Face Detection and Recognition Methods

Authors(3) :-Kanika Bhatia, Prof. Umesh Kumar Lilhore, Prof. Nitin Agrawal

Image processing methods play a vital role in different applications, face detection and reorganization is one of them. In recent technology, the popularity and demand of image processing are increasing due to its immense number of application in various fields. Most of these are related to biometric science like face recognition, fingerprint recognition, iris scan, and speech recognition. Among them, face detection is a very powerful tool for video surveillance, human computer interface, face recognition, and image database management. There are a different number of works on this subject. Face recognition is a rapidly evolving technology, which has been widely used in forensics such as criminal identification, secured access, and prison security. The human face is a dynamic object and has a high degree of variability in its appearance, which makes face detection a difficult problem in computer vision. A wide variety of techniques have been proposed, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods. Various researchers have been suggested different human face detection and reorganization method for various application decades. This review paper presents a comparative analysis of various face detection and reorganization methods.

Authors and Affiliations

Kanika Bhatia
M. Tech. Research Scholar, NRI Institute of Information Science & Technology Bhopal, Madhya Pradesh, India
Prof. Umesh Kumar Lilhore
Head PG, NRI Institute of Information Science & Technology Bhopal, Madhya Pradesh, India
Prof. Nitin Agrawal
Associate Professor, NRI Institute of Information Science & Technology Bhopal, Madhya Pradesh, India

Face Detection; Face Localization; Facial Feature Detection; Feature Based Approaches; Image-Based Approaches

  1. Michel Owayjan1, 2, Roger Achkar2, Moussa Iskandar2," Face Detection with Expression Recognition using Artificial Neural Networks", 2016 3rd Middle East Conference on Biomedical Engineering (MECBME), IEEE 2016 PP 978-983.
  2. Wonjun Kim,1 Chanho Jung,2 and Simone Bianco, “Optimization for Detection and Recognition in Images and Videos", Hindawi Mathematical Problems in Engineering Volume 2017, Article ID 5190490, 401-403
  3. G.Suvarna Kumar P.V.G.D Prasad Reddy R.Anil Kumar Sumit Gupta," Position Detection with Face Recognition using Image Processing and Machine Learning Techniques", IJCA Special Issue on “Novel Aspects of Digital Imaging Applications" DIA, 2011, PP 79-88
  4. Wilson, Phillip Ian, and John Fernandez. "Facial feature detection using Haar classifiers." Journal of Computing Sciences in Colleges 21.4 (2006): 127-133.
  5. Scheenstra, Alize, Arnout Ruifrok, and Remco C. Veltkamp. "A survey of 3D face recognition methods." Audio-and Video-Based Biometric Person Authentication. Springer Berlin Heidelberg, (2005).
  6. Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.
  7. Jafri, Rabia, and Hamid R. Arabnia. "A Survey of Face Recognition Techniques." JIPS 5.2 (2009): 41-68.
  8. Al-Ghamdi, Bayan Ali Saad, Sumayyah Redhwan Allaam, and Safeeullah Soomro. "Recognition of Human Face by Face Recognition System using 3D." Journal of Information & Communication Technology Vol 4: 27-34.
  9. Siddharth Swarup Rautaray and Anupam Agrawal, "Real Time Multiple Hand Gesture Recognition System for Human-Computer Interaction, In International Journal of Intelligent Systems and Applications, 2012, 5, 56-64, DOI: 10.5815/ijisa.2012.05.08
  10. Song, Fengxi, et al. "A multiple maxima scatter difference discriminant criterion for facial feature extraction." Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 37.6 (2007): 1599- 1606.
  11. Zhao, Wenyi, et al. "Face recognition: A literature survey." Acm Computing Surveys (CSUR) (2003): 399-458.
  12. Abate, Andrea F., et al. "2D and 3D face recognition: A survey." Pattern Recognition Letters 28.14 (2007): 1885- 1906.
  13. Colombo, Alessandro, Claudio Cusano, and Raimondo Schettini. "3D face detection using curvature analysis." Pattern recognition (2006): 444-455.
  14. Zhou, Xuebing, et al. "A 3d face recognition algorithm using histogram-based features." Proceedings of the 1st Eurographics conference on 3D Object Retrieval. Eurographics Association, 2008.
  15. Sadi, Vural. "Face recognition by using hybrid-holistic methods for outdoor surveillance systems." (2012).
  16. Belhumeur, Peter N. "Ongoing Challenges in Face Recognition." Frontiers of Engineering: Papers on Leading-Edge Engineering from the 2005 Symposium. 2005.
  17. Nigam, Aditya. A Novel Face Recognition Approach using Normalized Unmatched Points Measure. Diss. INDIAN INSTITUTE OF TECHNOLOGY, 2009.
  18. Vully, Mahesh Kumar. Facial expression detection using principal component analysis. Diss. 2011.
  19. Fladsrud, Tom, and False Acceptance Rate. "Face Recognition in a border control environment." Gjøvik University College (2005).
  20. Patra, Arpita. "Development of efficient methods for face recognition and multimodal biometry." (2006).
  21. Vucini, Emerald, Muhittin Gökmen, and Eduard Gröller. "Face recognition under varying illumination." Proceedings WSCG. 2007.

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 595-600
Manuscript Number : CSEIT1725137
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Kanika Bhatia, Prof. Umesh Kumar Lilhore, Prof. Nitin Agrawal, "Review of Different Face Detection and Recognition Methods", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.595-600, September-October-2017.
Journal URL :

Article Preview